The Paper-Explainer Skill
How It Works and How It Helps Newcomers Navigate Technical Literature
Purpose: Orientation guide for analysts new to electricity markets
Illustrative example: Vaishampayan, Saurabh, and Maryam Kamgarpour. ‘Uncertainty and Autarky: Cooperative Game Theory for Stable Local Energy Market Partitioning’. arXiv.Org, 5 March 2026. https://arxiv.org/abs/2603.05169v2.
Background: The Problem of Technical Literature
I am currently reading a fascinating, but highly technical, paper about an experimental decentralised electricity distribution grid design in Switzerland. Apparently, in Switzerland, local neighbourhoods are allowed to form small microgrids to self-supply electricity using household and small commercial sites that host electricity generators, e.g., photovoltaic arrays.
Given the widespread adoption of household solar panels and battery storage systems, this seems relevant to Australia. One immediate issue, though, is that this paper is dense and difficult to comprehend.
Academic papers in electricity market analysis are written for specialist audiences. A paper on local energy market design, for instance, may assume fluency in power systems engineering, cooperative game theory, and optimisation mathematics — three separate disciplines — before the reader reaches the first result. For someone new to the field, the terminology alone can be a barrier, let alone the mathematical formulations.
Luckily, I have long used a variety of learning techniques to develop sufficient comprehension. Given the proliferation of AI tools, I distilled these techniques into a set of so-called skills to automate parts of this process.
The paper-explainer skill, as I have named it, systematically decomposes a technical paper into structured learning artefacts. Each artefact is designed to build understanding in a different way, from vocabulary to logic to plain-language translation. Together, this gives me, and any newcomer using these techniques, a working conceptual map of the paper before engaging with its equations or proofs.
What the Paper-Explainer Skill is
The paper-explainer skill is a structured prompt-engineering template that I added to my AI assistant (Claude). When activated using the command /paper-explainer, it instructs the assistant to analyse an uploaded paper and produce five specific outputs, called artefacts, in a fixed sequence.
The skill is configured with three design principles:
Audience-first writing. Every explanation is written as if the reader has no prior knowledge of the field. Jargon terms are defined, not assumed.
Accuracy before simplicity. Simplification must not distort the paper’s actual claims. Where a concept resists easy summary, the artefact says so rather than offering a misleading shortcut.
Structured rather than narrative. The outputs use tables, bullet points, and parallel formats deliberately, so that a reader can scan, compare, and return to specific sections without re-reading the whole document.
The Five Artefacts and What Each One Does
Artefact 1: Key Concepts Table
What it is: A table of 8–15 concepts that are essential for understanding the paper’s argument, with a plain-language explanation of each and a note on why it appears in this particular paper.
Why it helps: A paper’s argument rests on a specific set of concepts. Not all unfamiliar words are equally important. Some are central load-bearing ideas; others are incidental notation. The key concepts table forces a prioritisation. A newcomer reading it gains a working mental model of the paper’s intellectual territory before encountering a single equation.
Example from the Vaishampayan & Kamgarpour paper:
One entry in the table is autarky, which in everyday usage means a national economy cutting itself off from trade. In this paper, autarky refers to an energy coalition that generates and consumes its own electricity internally, minimising power exchange with the wider grid. The table explains this meaning immediately and notes that autarky is the central behavioural assumption driving the partitioning results. Without understanding autarky in this sense, the paper’s core logic is opaque.
Artefact 2: Key Terms List (20 Terms, 5 Categories)
What it is: Twenty terms from the paper grouped into five thematic categories, each with a one-sentence plain-language definition.
Why it helps: Vocabulary is the entry point to any technical field. This artefact functions like a mini-glossary, but the categorisation step adds an extra layer of value, showing how different types of concepts relate to each other. A newcomer learns both what each term means and the disciplinary domain they come from — engineering, economics, mathematics, or policy.
Example from the paper:
The paper draws on three distinct bodies of knowledge: power systems engineering (voltage deviation, Linear DistFlow equations, radial network), cooperative game theory (coalition, core, stable partition), and optimisation (ex-ante dispatch, Monte Carlo simulation, Distribution Locational Marginal Price). By grouping terms this way, the artefact signals to the reader that understanding the paper requires fluency with ideas from all three domains simultaneously, which is what makes it difficult, and why the artefact is structured to unpack them one domain at a time.
Artefact 3: Step-by-Step Definitions
What it is: For 5–8 of the paper’s most technically dense definitions, a restatement broken into sequential steps that a non-expert can follow one at a time.
Why it helps: Academic definitions are often written in a compressed, formal style that assumes the reader already knows most of what is being defined. Unpacking them into steps makes the logical structure visible. It also identifies where a definition requires a prerequisite idea — something the compressed original often obscures.
Example from the paper:
The paper’s definition of a stable partition (Definition V.2) reads, in its original form, as a single formal statement: a partition is stable if and only if the core is non-empty for every coalition in the partition. This definition contains three embedded concepts — partition, core, and non-emptiness — each of which is itself formally defined elsewhere. The step-by-step rewrite resolves this layering: it begins by recalling what a partition is, explains what it means for a coalition’s core to be nonempty, and then shows that this condition must hold simultaneously for every coalition. A newcomer following the steps arrives at the correct understanding without needing to hold all three definitions in mind at once.
Artefact 4: Propositions Table
What it is: A table of 10–15 key claims from the paper, expressed as explicit relationships between concepts, with the type of relationship labelled (causation, dependency, mechanism, classification, etc).
Why it helps: Understanding what a paper claims is as important as understanding its vocabulary. Many newcomers read a paper and come away with a vague sense of the topic but no clear grasp of what the paper actually argues. The propositions table converts the paper’s claims into short, testable statements. It also reveals the structure of the argument — which claims are foundational, which are derived, and which are the main results.
Example from the paper:
One proposition extracted from the paper states: As prosumption forecast noise increases, optimal stable partitions fragment into smaller LEMs (Mechanism). This is the paper’s central empirical finding, captured in Table IV of the original. The propositions table links this result to its cause (higher forecast error raises ex-post voltage violation costs, which grow faster in larger coalitions) and to its implication (as forecasting technology improves, larger markets become both more efficient and more stable). A newcomer reading only the abstract would not see this logical chain; the propositions table makes it explicit.
Artefact 5: Plain-Language Rewrites
What it is: Five to eight of the paper’s most technically dense sentences, each reproduced in its original form and then rewritten in simpler language.
Why it helps: Reading a difficult paper is often a sentence-by-sentence struggle. Newcomers frequently hit a sentence they cannot parse and either skip it (losing the thread) or spend disproportionate time on it. The plain-language rewrites pre-process the hardest sentences in the paper so that the reader can move through the argument without getting stuck. They also model how to read technical prose, showing which parts of a dense sentence convey the core meaning and which serve as formal scaffolding.
Example from the paper:
One of the rewritten sentences concerns externalities in power flow: the original states that “power flow constraints introduce coupling between LEMs, meaning that LEMs’ costs not only depend on their internal configuration, but may also depend on the configuration of other LEMs in the grid.”
The rewrite explains this with a concrete physical analogy, describing voltage as water pressure in pipes: if someone upstream changes how much water they draw, the pressure at your tap changes too, even if you did nothing. This analogy is not in the paper, but it captures the physics and makes the concept immediately intuitive for someone without an engineering background.
How to Use the Skill in Practice
The /paper-explainer skill is activated by uploading a PDF of the paper to the AI assistant and typing /paper-explainer. The assistant reads the full paper and produces all five artefacts in a single response.
The recommended reading sequence for a newcomer is:
Read Artefact 1 (Key Concepts Table) first. This gives you a mental map of the paper before you open it.
Skim Artefact 2 (Key Terms) next. Use it as a reference glossary while reading — return to it whenever an unfamiliar term appears.
Read the paper’s abstract and conclusion before engaging with the body. These sections state the main claims without the mathematical detail.
Use Artefact 3 (Step-by-Step Definitions) as a companion when you encounter the paper’s formal definitions. Read the original, then read the step-by-step rewrite immediately after.
Use Artefact 4 (Propositions Table) to check your comprehension after reading each section. Can you locate the proposition in the section you just read?
Use Artefact 5 (Plain-Language Rewrites) on demand. If you hit a sentence you cannot parse, check whether it appears in Artefact 5.
Scope and Limitations
The /paper-explainer skill is a comprehension tool, not a substitute for reading the paper itself. There is no evaluation of the paper’s methodology, critique of its assumptions, or assessment of the quality of its evidence. For that kind of critical analysis, a separate set of tools is required.
The skill is also calibrated to the paper’s full text. It works best when the entire document is uploaded, because it needs to identify which concepts are load-bearing for the specific argument being made, not just which terms appear most frequently.
Finally, the plain-language rewrites are designed to be accurate simplifications rather than perfect translations. Some mathematical concepts, such as the non-emptiness of a core or the supermodularity of a cost function, have no everyday English equivalent that carries the full technical meaning. In those cases, the rewrite explains the practical implications of the concept rather than its formal content, and explicitly notes the gap.
Conclusion
Technical papers in electricity market analysis combine engineering, economics, and mathematics in ways that are difficult for newcomers to unpack. The /paper-explainer skill structures this unpacking into five complementary artefacts, concepts, vocabulary, definitions, propositions, and plain-language rewrites, each targeting a different layer of the comprehension problem.
Applied to the Vaishampayan and Kamgarpour (2026) paper on local energy market partitioning, the skill translated the paper, which combines radial power flow physics, cooperative game theory, and stochastic optimisation, into a set of accessible learning tools. The central finding — that uncertainty drives a preference for smaller, more self-sufficient energy coalitions — is now accessible to a reader who does not know what a Linear DistFlow equation is, or what it means for a core to be non-empty.
For analysts building familiarity with contemporary electricity market research, the skill offers a practical on-ramp, removing the vocabulary barrier that can otherwise make that work feel insurmountable.
I have placed the full paper-explainer specification in the appendix for those who wish to try it for themselves.
APPENDIX
For those who would like to implement or improve this skill, here is the full skill specification.
---
name: paper-explainer
description: >
Analyse and explain any academic, technical, or policy paper for a non-expert
audience by producing five structured artefacts: a key-concepts table, a
categorised key-terms list, bullet-point definitions, a propositions table, and
plain-language rewrites of technical sentences. Trigger this skill whenever the
user shares a paper, document, or dense text and asks to “explain”, “break down”,
“analyse”, “summarise”, or “make sense of” it — or when they ask for any of the
five artefacts by name. Also trigger when the user uploads or pastes a paper and
asks general comprehension questions. Apply aggressively: if there is academic or
technical text in context and the user wants help understanding it, this skill
applies.
---
# Paper Explainer
You are an expert in analytical and critical thinking and an educator skilled at
explaining complex concepts to beginners. Your job is to take any academic, technical,
or policy paper and produce five structured artefacts that make it accessible to a
non-expert reader.
---
## Inputs
The user will provide one of:
- A pasted block of text from a paper
- An uploaded PDF or document (read it first)
- A URL to a paper (fetch it first)
- A file already in context
If the source is unclear, ask before proceeding.
---
## The Five Artefacts
Produce **all five artefacts** in a single response, in the order below. Use clear
Markdown headers for each. If the paper is very short or a single section, scale the
depth proportionally (e.g., 10 key terms instead of 20 is fine for a short excerpt).
---
### Artefact 1 — Key Concepts Table
A table of the most important concepts needed to understand the paper.
| Concept | Plain-language explanation | Why it matters in this paper |
|---------|---------------------------|------------------------------|
| ... | ... | ... |
Aim for 8–15 rows. Prioritise concepts that are load-bearing for the paper’s argument.
---
### Artefact 2 — Key Terms (20 terms, 5 categories)
List 20 key terms from the paper grouped into exactly five thematic categories.
Name each category to reflect its theme. Format:
**Category 1: [Name]**
1. Term — one-sentence definition in plain language
2. ...
Repeat for all five categories (4 terms each).
---
### Artefact 3 — Step-by-Step Definitions
Take the definitions the paper gives for its core terms and rephrase them as bullet
points so a non-expert can follow them step by step.
For each term:
**[Term]**
- Step 1: ...
- Step 2: ...
- Step 3: ...
Aim for 5–8 terms. Choose the ones whose original definitions are most dense or
jargon-heavy.
---
### Artefact 4 — Propositions Table
Extract the key propositions from the paper and express them in relational form.
| Proposition | Type | Example from paper |
|------------|------|--------------------|
| X is a type of Y | Classification | ... |
| W is caused by X | Causation | ... |
| A explains B | Explanatory | ... |
Aim for 10–15 rows. Use these relation types (and others as needed):
- “X is a type of Y” — classification / taxonomy
- “W is caused by X” — causation
- “A explains B” — explanatory / predictive
- “P depends on Q” — dependency
- “M is measured by N” — operationalisation
- “R leads to S” — mechanism / process
---
### Artefact 5 — Plain-Language Rewrites
Select 5–8 of the most technically dense sentences from the paper. For each:
> **Original:** [exact sentence]
**Plain language:** [rewrite in simpler language; break into multiple sentences if
needed; avoid jargon or define it inline]
---
## Quality Standards
- Never use the jargon term to define itself.
- If a sentence needs more than two clauses to simplify, split it.
- In the propositions table, every row must be a falsifiable claim — not a vague
statement.
- In the key terms list, definitions must be self-contained (a reader who has never
seen the paper could understand them).
- Maintain accuracy: simplification must not distort the paper’s meaning.
---
## Notes on Partial Requests
If the user asks for only one or two of the five artefacts (e.g., “just give me the
propositions table”), produce only those. Still apply the same quality standards.